CBTI Summary

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Table

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isi

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict isi with group and time_point (formula: isi ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 0.26. The model’s intercept, corresponding to group = control and time_point = 1st, is at 13.53 (95% CI [12.97, 14.09], t(850) = 47.32, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.13, 95% CI [-0.92, 0.66], t(850) = -0.32, p = 0.751; Std. beta = -0.03, 95% CI [-0.21, 0.15])
• The effect of time point [2nd] is statistically significant and negative (beta = -2.46, 95% CI [-3.09, -1.83], t(850) = -7.62, p < .001; Std. beta = -0.55, 95% CI [-0.69, -0.41])
• The effect of time point [3rd] is statistically significant and negative (beta = -2.87, 95% CI [-3.52, -2.22], t(850) = -8.69, p < .001; Std. beta = -0.64, 95% CI [-0.78, -0.50])
• The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.96, 95% CI [-3.92, -2.01], t(850) = -6.10, p < .001; Std. beta = -0.66, 95% CI [-0.87, -0.45])
• The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.96, 95% CI [-3.93, -1.99], t(850) = -5.99, p < .001; Std. beta = -0.66, 95% CI [-0.88, -0.44])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

who

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict who with group and time_point (formula: who ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.61) and the part related to the fixed effects alone (marginal R2) is of 0.05. The model’s intercept, corresponding to group = control and time_point = 1st, is at 9.82 (95% CI [9.22, 10.42], t(850) = 32.18, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.16, 95% CI [-0.68, 1.01], t(850) = 0.38, p = 0.707; Std. beta = 0.04, 95% CI [-0.16, 0.24])
• The effect of time point [2nd] is statistically significant and positive (beta = 0.73, 95% CI [0.14, 1.31], t(850) = 2.44, p = 0.015; Std. beta = 0.17, 95% CI [0.03, 0.31])
• The effect of time point [3rd] is statistically significant and positive (beta = 0.93, 95% CI [0.33, 1.53], t(850) = 3.04, p = 0.002; Std. beta = 0.22, 95% CI [0.08, 0.36])
• The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 1.40, 95% CI [0.51, 2.29], t(850) = 3.10, p = 0.002; Std. beta = 0.33, 95% CI [0.12, 0.54])
• The interaction effect of time point [3rd] on group [treatment] is statistically significant and positive (beta = 1.63, 95% CI [0.72, 2.53], t(850) = 3.53, p < .001; Std. beta = 0.39, 95% CI [0.17, 0.60])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

phq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict phq with group and time_point (formula: phq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.68) and the part related to the fixed effects alone (marginal R2) is of 0.04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 8.21 (95% CI [7.47, 8.95], t(850) = 21.70, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.59, 95% CI [-0.46, 1.64], t(850) = 1.11, p = 0.268; Std. beta = 0.11, 95% CI [-0.09, 0.32])
• The effect of time point [2nd] is statistically significant and negative (beta = -0.78, 95% CI [-1.43, -0.13], t(850) = -2.34, p = 0.019; Std. beta = -0.15, 95% CI [-0.28, -0.02])
• The effect of time point [3rd] is statistically non-significant and negative (beta = -0.66, 95% CI [-1.33, 5.02e-03], t(850) = -1.95, p = 0.052; Std. beta = -0.13, 95% CI [-0.26, 9.66e-04])
• The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.73, 95% CI [-2.73, -0.74], t(850) = -3.43, p < .001; Std. beta = -0.33, 95% CI [-0.53, -0.14])
• The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.42, 95% CI [-3.43, -1.41], t(850) = -4.69, p < .001; Std. beta = -0.47, 95% CI [-0.66, -0.27])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

gad

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict gad with group and time_point (formula: gad ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 0.04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 7.54 (95% CI [6.79, 8.28], t(850) = 19.75, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.48, 95% CI [-0.58, 1.54], t(850) = 0.89, p = 0.373; Std. beta = 0.09, 95% CI [-0.11, 0.30])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.44, 95% CI [-1.11, 0.23], t(850) = -1.29, p = 0.197; Std. beta = -0.08, 95% CI [-0.21, 0.04])
• The effect of time point [3rd] is statistically non-significant and negative (beta = -0.58, 95% CI [-1.27, 0.10], t(850) = -1.66, p = 0.097; Std. beta = -0.11, 95% CI [-0.24, 0.02])
• The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.07, 95% CI [-3.09, -1.06], t(850) = -4.01, p < .001; Std. beta = -0.40, 95% CI [-0.60, -0.20])
• The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.38, 95% CI [-3.42, -1.35], t(850) = -4.52, p < .001; Std. beta = -0.46, 95% CI [-0.66, -0.26])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

wsas

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict wsas with group and time_point (formula: wsas ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.64) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 16.77 (95% CI [15.30, 18.24], t(850) = 22.41, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.08, 95% CI [-2.16, 1.99], t(850) = -0.08, p = 0.937; Std. beta = -8.25e-03, 95% CI [-0.21, 0.20])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.82, 95% CI [-2.18, 0.54], t(850) = -1.18, p = 0.238; Std. beta = -0.08, 95% CI [-0.21, 0.05])
• The effect of time point [3rd] is statistically non-significant and negative (beta = -0.13, 95% CI [-1.53, 1.26], t(850) = -0.18, p = 0.853; Std. beta = -0.01, 95% CI [-0.15, 0.12])
• The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.95, 95% CI [-5.02, -0.89], t(850) = -2.80, p = 0.005; Std. beta = -0.29, 95% CI [-0.49, -0.09])
• The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -4.90, 95% CI [-7.01, -2.80], t(850) = -4.57, p < .001; Std. beta = -0.48, 95% CI [-0.69, -0.28])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

shps_arousal

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_arousal with group and time_point (formula: shps_arousal ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.56) and the part related to the fixed effects alone (marginal R2) is of 0.11. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.02 (95% CI [2.91, 3.13], t(850) = 54.49, p < .001). Within this model:

• The effect of group [treatment] is statistically significant and positive (beta = 0.16, 95% CI [8.91e-03, 0.32], t(850) = 2.07, p = 0.038; Std. beta = 0.21, 95% CI [0.01, 0.40])
• The effect of time point [2nd] is statistically significant and negative (beta = -0.20, 95% CI [-0.31, -0.08], t(850) = -3.30, p < .001; Std. beta = -0.25, 95% CI [-0.39, -0.10])
• The effect of time point [3rd] is statistically significant and negative (beta = -0.22, 95% CI [-0.34, -0.10], t(850) = -3.64, p < .001; Std. beta = -0.28, 95% CI [-0.43, -0.13])
• The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.48, 95% CI [-0.65, -0.30], t(850) = -5.34, p < .001; Std. beta = -0.60, 95% CI [-0.83, -0.38])
• The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.56, 95% CI [-0.74, -0.38], t(850) = -6.18, p < .001; Std. beta = -0.71, 95% CI [-0.94, -0.49])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

shps_schedule

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_schedule with group and time_point (formula: shps_schedule ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 0.05. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.53 (95% CI [3.40, 3.66], t(850) = 53.14, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.04, 95% CI [-0.14, 0.23], t(850) = 0.44, p = 0.659; Std. beta = 0.05, 95% CI [-0.16, 0.25])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.10, 95% CI [-0.22, 0.02], t(850) = -1.68, p = 0.093; Std. beta = -0.11, 95% CI [-0.24, 0.02])
• The effect of time point [3rd] is statistically significant and negative (beta = -0.14, 95% CI [-0.26, -0.02], t(850) = -2.21, p = 0.027; Std. beta = -0.15, 95% CI [-0.28, -0.02])
• The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.34, 95% CI [-0.52, -0.17], t(850) = -3.79, p < .001; Std. beta = -0.38, 95% CI [-0.57, -0.18])
• The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.42, 95% CI [-0.60, -0.24], t(850) = -4.55, p < .001; Std. beta = -0.46, 95% CI [-0.66, -0.26])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

shps_behavior

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_behavior with group and time_point (formula: shps_behavior ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.58) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 1.99 (95% CI [1.89, 2.08], t(850) = 39.01, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.13, 95% CI [-8.82e-03, 0.27], t(850) = 1.84, p = 0.066; Std. beta = 0.19, 95% CI [-0.01, 0.40])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.02, 95% CI [-0.07, 0.12], t(850) = 0.48, p = 0.629; Std. beta = 0.04, 95% CI [-0.11, 0.18])
• The effect of time point [3rd] is statistically non-significant and positive (beta = 9.29e-03, 95% CI [-0.09, 0.11], t(850) = 0.18, p = 0.858; Std. beta = 0.01, 95% CI [-0.13, 0.16])
• The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.24, 95% CI [-0.39, -0.09], t(850) = -3.18, p = 0.001; Std. beta = -0.35, 95% CI [-0.57, -0.14])
• The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.33, 95% CI [-0.49, -0.18], t(850) = -4.26, p < .001; Std. beta = -0.48, 95% CI [-0.71, -0.26])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

shps_environment

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict shps_environment with group and time_point (formula: shps_environment ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 2.33 (95% CI [2.21, 2.45], t(850) = 38.46, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.23, 0.11], t(850) = -0.72, p = 0.469; Std. beta = -0.08, 95% CI [-0.28, 0.13])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.18, 0.06], t(850) = -0.97, p = 0.330; Std. beta = -0.07, 95% CI [-0.22, 0.07])
• The effect of time point [3rd] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.18, 0.06], t(850) = -0.97, p = 0.333; Std. beta = -0.07, 95% CI [-0.22, 0.07])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.09, 95% CI [-0.26, 0.09], t(850) = -0.94, p = 0.346; Std. beta = -0.10, 95% CI [-0.32, 0.11])
• The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.26, 95% CI [-0.44, -0.08], t(850) = -2.81, p = 0.005; Std. beta = -0.32, 95% CI [-0.54, -0.10])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

dbas_consequence

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_consequence with group and time_point (formula: dbas_consequence ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.62) and the part related to the fixed effects alone (marginal R2) is of 0.12. The model’s intercept, corresponding to group = control and time_point = 1st, is at 6.59 (95% CI [6.31, 6.86], t(850) = 46.92, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.05, 95% CI [-0.34, 0.44], t(850) = 0.27, p = 0.787; Std. beta = 0.03, 95% CI [-0.17, 0.22])
• The effect of time point [2nd] is statistically significant and negative (beta = -0.34, 95% CI [-0.61, -0.06], t(850) = -2.39, p = 0.017; Std. beta = -0.17, 95% CI [-0.30, -0.03])
• The effect of time point [3rd] is statistically significant and negative (beta = -0.67, 95% CI [-0.95, -0.39], t(850) = -4.64, p < .001; Std. beta = -0.33, 95% CI [-0.47, -0.19])
• The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.11, 95% CI [-1.53, -0.69], t(850) = -5.21, p < .001; Std. beta = -0.55, 95% CI [-0.76, -0.34])
• The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -1.30, 95% CI [-1.72, -0.87], t(850) = -5.98, p < .001; Std. beta = -0.65, 95% CI [-0.86, -0.43])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

dbas_worry

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_worry with group and time_point (formula: dbas_worry ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.53) and the part related to the fixed effects alone (marginal R2) is of 0.16. The model’s intercept, corresponding to group = control and time_point = 1st, is at 14.20 (95% CI [13.65, 14.76], t(850) = 50.09, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.34, 95% CI [-0.45, 1.13], t(850) = 0.85, p = 0.395; Std. beta = 0.08, 95% CI [-0.11, 0.27])
• The effect of time point [2nd] is statistically significant and negative (beta = -1.23, 95% CI [-1.86, -0.60], t(850) = -3.81, p < .001; Std. beta = -0.30, 95% CI [-0.45, -0.14])
• The effect of time point [3rd] is statistically significant and negative (beta = -1.83, 95% CI [-2.48, -1.18], t(850) = -5.54, p < .001; Std. beta = -0.44, 95% CI [-0.60, -0.29])
• The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -2.71, 95% CI [-3.67, -1.76], t(850) = -5.58, p < .001; Std. beta = -0.65, 95% CI [-0.88, -0.42])
• The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.88, 95% CI [-3.85, -1.91], t(850) = -5.81, p < .001; Std. beta = -0.69, 95% CI [-0.93, -0.46])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

dbas_expectation

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_expectation with group and time_point (formula: dbas_expectation ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 0.11. The model’s intercept, corresponding to group = control and time_point = 1st, is at 7.17 (95% CI [6.84, 7.51], t(850) = 41.60, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.28, 95% CI [-0.76, 0.19], t(850) = -1.17, p = 0.243; Std. beta = -0.12, 95% CI [-0.31, 0.08])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.34, 95% CI [-0.69, 1.34e-03], t(850) = -1.95, p = 0.051; Std. beta = -0.14, 95% CI [-0.28, 5.48e-04])
• The effect of time point [3rd] is statistically significant and negative (beta = -0.78, 95% CI [-1.13, -0.42], t(850) = -4.31, p < .001; Std. beta = -0.32, 95% CI [-0.46, -0.17])
• The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.25, 95% CI [-1.77, -0.73], t(850) = -4.70, p < .001; Std. beta = -0.51, 95% CI [-0.72, -0.30])
• The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -1.28, 95% CI [-1.81, -0.75], t(850) = -4.72, p < .001; Std. beta = -0.52, 95% CI [-0.74, -0.31])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

dbas_medication

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict dbas_medication with group and time_point (formula: dbas_medication ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.56) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.15 (95% CI [2.83, 3.46], t(850) = 19.55, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.09, 95% CI [-0.36, 0.54], t(850) = 0.39, p = 0.695; Std. beta = 0.04, 95% CI [-0.17, 0.25])
• The effect of time point [2nd] is statistically significant and positive (beta = 0.37, 95% CI [0.04, 0.69], t(850) = 2.23, p = 0.026; Std. beta = 0.17, 95% CI [0.02, 0.32])
• The effect of time point [3rd] is statistically non-significant and positive (beta = 0.31, 95% CI [-0.02, 0.64], t(850) = 1.83, p = 0.067; Std. beta = 0.14, 95% CI [-0.01, 0.30])
• The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.66, 95% CI [-1.15, -0.18], t(850) = -2.67, p = 0.008; Std. beta = -0.31, 95% CI [-0.53, -0.08])
• The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.86, 95% CI [-1.35, -0.36], t(850) = -3.39, p < .001; Std. beta = -0.40, 95% CI [-0.63, -0.17])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

psas_somatic

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict psas_somatic with group and time_point (formula: psas_somatic ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.63) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 1.86 (95% CI [1.76, 1.96], t(850) = 36.76, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.04, 95% CI [-0.10, 0.19], t(850) = 0.62, p = 0.533; Std. beta = 0.07, 95% CI [-0.14, 0.27])
• The effect of time point [2nd] is statistically significant and positive (beta = 0.14, 95% CI [0.05, 0.24], t(850) = 3.03, p = 0.002; Std. beta = 0.21, 95% CI [0.07, 0.35])
• The effect of time point [3rd] is statistically non-significant and positive (beta = 8.14e-03, 95% CI [-0.09, 0.10], t(850) = 0.17, p = 0.867; Std. beta = 0.01, 95% CI [-0.13, 0.15])
• The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.31, 95% CI [-0.45, -0.17], t(850) = -4.27, p < .001; Std. beta = -0.45, 95% CI [-0.65, -0.24])
• The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.24, 95% CI [-0.38, -0.10], t(850) = -3.28, p = 0.001; Std. beta = -0.35, 95% CI [-0.56, -0.14])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

psas_cognitive

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict psas_cognitive with group and time_point (formula: psas_cognitive ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 0.09. The model’s intercept, corresponding to group = control and time_point = 1st, is at 2.87 (95% CI [2.75, 3.00], t(850) = 45.28, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.10, 95% CI [-0.08, 0.27], t(850) = 1.11, p = 0.269; Std. beta = 0.11, 95% CI [-0.09, 0.31])
• The effect of time point [2nd] is statistically significant and negative (beta = -0.20, 95% CI [-0.33, -0.08], t(850) = -3.20, p = 0.001; Std. beta = -0.23, 95% CI [-0.37, -0.09])
• The effect of time point [3rd] is statistically significant and negative (beta = -0.36, 95% CI [-0.48, -0.23], t(850) = -5.44, p < .001; Std. beta = -0.40, 95% CI [-0.54, -0.26])
• The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.43, 95% CI [-0.62, -0.24], t(850) = -4.49, p < .001; Std. beta = -0.49, 95% CI [-0.70, -0.28])
• The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.41, 95% CI [-0.61, -0.22], t(850) = -4.21, p < .001; Std. beta = -0.47, 95% CI [-0.68, -0.25])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

psqi_global

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict psqi_global with group and time_point (formula: psqi_global ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.56) and the part related to the fixed effects alone (marginal R2) is of 0.15. The model’s intercept, corresponding to group = control and time_point = 1st, is at 10.72 (95% CI [10.26, 11.18], t(850) = 45.26, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.29, 95% CI [-0.37, 0.95], t(850) = 0.87, p = 0.386; Std. beta = 0.08, 95% CI [-0.11, 0.27])
• The effect of time point [2nd] is statistically significant and negative (beta = -1.31, 95% CI [-1.82, -0.81], t(850) = -5.10, p < .001; Std. beta = -0.38, 95% CI [-0.53, -0.23])
• The effect of time point [3rd] is statistically significant and negative (beta = -1.32, 95% CI [-1.84, -0.80], t(850) = -4.99, p < .001; Std. beta = -0.38, 95% CI [-0.53, -0.23])
• The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -1.86, 95% CI [-2.62, -1.10], t(850) = -4.79, p < .001; Std. beta = -0.54, 95% CI [-0.76, -0.32])
• The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -2.44, 95% CI [-3.22, -1.66], t(850) = -6.16, p < .001; Std. beta = -0.71, 95% CI [-0.93, -0.48])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

mic_attention

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict mic_attention with group and time_point (formula: mic_attention ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.60) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 1.30 (95% CI [1.19, 1.41], t(850) = 22.91, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.12, 95% CI [-0.04, 0.28], t(850) = 1.52, p = 0.129; Std. beta = 0.16, 95% CI [-0.05, 0.36])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.02, 95% CI [-0.13, 0.09], t(850) = -0.39, p = 0.693; Std. beta = -0.03, 95% CI [-0.17, 0.11])
• The effect of time point [3rd] is statistically non-significant and positive (beta = 0.03, 95% CI [-0.08, 0.14], t(850) = 0.50, p = 0.614; Std. beta = 0.04, 95% CI [-0.11, 0.18])
• The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.25, 95% CI [-0.41, -0.08], t(850) = -2.95, p = 0.003; Std. beta = -0.32, 95% CI [-0.54, -0.11])
• The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.38, 95% CI [-0.55, -0.21], t(850) = -4.47, p < .001; Std. beta = -0.50, 95% CI [-0.72, -0.28])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

mic_executive

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict mic_executive with group and time_point (formula: mic_executive ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.63) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 1.28 (95% CI [1.17, 1.39], t(850) = 22.00, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.07, 95% CI [-0.09, 0.23], t(850) = 0.82, p = 0.415; Std. beta = 0.08, 95% CI [-0.12, 0.29])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.03, 95% CI [-0.14, 0.07], t(850) = -0.62, p = 0.537; Std. beta = -0.04, 95% CI [-0.18, 0.09])
• The effect of time point [3rd] is statistically non-significant and negative (beta = -0.05, 95% CI [-0.16, 0.06], t(850) = -0.85, p = 0.394; Std. beta = -0.06, 95% CI [-0.20, 0.08])
• The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.16, 95% CI [-0.32, 2.33e-03], t(850) = -1.93, p = 0.053; Std. beta = -0.20, 95% CI [-0.41, 2.95e-03])
• The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.27, 95% CI [-0.44, -0.11], t(850) = -3.25, p = 0.001; Std. beta = -0.35, 95% CI [-0.55, -0.14])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

mic_memory

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict mic_memory with group and time_point (formula: mic_memory ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 1.33 (95% CI [1.22, 1.44], t(850) = 23.30, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.07, 95% CI [-0.09, 0.22], t(850) = 0.81, p = 0.417; Std. beta = 0.08, 95% CI [-0.12, 0.29])
• The effect of time point [2nd] is statistically non-significant and positive (beta = 0.03, 95% CI [-0.07, 0.13], t(850) = 0.62, p = 0.537; Std. beta = 0.04, 95% CI [-0.09, 0.17])
• The effect of time point [3rd] is statistically non-significant and negative (beta = -0.06, 95% CI [-0.16, 0.04], t(850) = -1.14, p = 0.253; Std. beta = -0.08, 95% CI [-0.21, 0.06])
• The interaction effect of time point [2nd] on group [treatment] is statistically significant and negative (beta = -0.28, 95% CI [-0.43, -0.12], t(850) = -3.56, p < .001; Std. beta = -0.36, 95% CI [-0.55, -0.16])
• The interaction effect of time point [3rd] on group [treatment] is statistically significant and negative (beta = -0.22, 95% CI [-0.38, -0.07], t(850) = -2.83, p = 0.005; Std. beta = -0.29, 95% CI [-0.49, -0.09])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_pcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_pcs with group and time_point (formula: nb_pcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.66) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 46.33 (95% CI [45.04, 47.63], t(850) = 70.36, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and negative (beta = -0.14, 95% CI [-1.96, 1.69], t(850) = -0.15, p = 0.881; Std. beta = -0.02, 95% CI [-0.22, 0.19])
• The effect of time point [2nd] is statistically non-significant and negative (beta = -0.87, 95% CI [-2.03, 0.28], t(850) = -1.48, p = 0.140; Std. beta = -0.10, 95% CI [-0.23, 0.03])
• The effect of time point [3rd] is statistically non-significant and negative (beta = -0.75, 95% CI [-1.94, 0.43], t(850) = -1.24, p = 0.213; Std. beta = -0.08, 95% CI [-0.22, 0.05])
• The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 2.76, 95% CI [1.00, 4.51], t(850) = 3.08, p = 0.002; Std. beta = 0.31, 95% CI [0.11, 0.51])
• The interaction effect of time point [3rd] on group [treatment] is statistically significant and positive (beta = 3.17, 95% CI [1.38, 4.96], t(850) = 3.47, p < .001; Std. beta = 0.35, 95% CI [0.15, 0.56])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_mcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_mcs with group and time_point (formula: nb_mcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.63) and the part related to the fixed effects alone (marginal R2) is of 0.06. The model’s intercept, corresponding to group = control and time_point = 1st, is at 39.90 (95% CI [38.39, 41.41], t(850) = 51.78, p < .001). Within this model:

• The effect of group [treatment] is statistically non-significant and positive (beta = 0.09, 95% CI [-2.05, 2.22], t(850) = 0.08, p = 0.938; Std. beta = 7.98e-03, 95% CI [-0.19, 0.21])
• The effect of time point [2nd] is statistically significant and positive (beta = 2.00, 95% CI [0.55, 3.45], t(850) = 2.71, p = 0.007; Std. beta = 0.19, 95% CI [0.05, 0.32])
• The effect of time point [3rd] is statistically significant and positive (beta = 2.30, 95% CI [0.81, 3.78], t(850) = 3.03, p = 0.002; Std. beta = 0.22, 95% CI [0.08, 0.35])
• The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 3.57, 95% CI [1.37, 5.77], t(850) = 3.19, p = 0.001; Std. beta = 0.33, 95% CI [0.13, 0.54])
• The interaction effect of time point [3rd] on group [treatment] is statistically significant and positive (beta = 4.63, 95% CI [2.40, 6.87], t(850) = 4.06, p < .001; Std. beta = 0.43, 95% CI [0.22, 0.64])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.